Good to Great How : Marine Harvest Can Innovate to Grow, and Grow to Innovate Amid Imperfect Industry Structure and Regulation
Bibliographic record
Abstract
Spatial conflicts exist between firms and farms in the Norwegian Salmon Farming industry which perpetuates sea lice and disease issues and prevents the industry from growing. Lack of growth in Norway in the face of increasing demand, increases prices and reveals national market power. As salmon are treated for sea lice more often, the fish grow less, and die more frequently thus exacerbating this effect. As a result, consumer surplus goes down and dead loss goes up as the industry becomes less efficient. Gaps in the underlying regulatory framework and lack of coordination among firms allows this to continue and thus jeopardizes sustainability of the industry. As a result, Marine Harvest Norway Farming (MHNF), being 20 – 25% of the industry volume, is disproportionately affected. This may put the Marine Harvest Group (MHG) strategy in jeopardy, as without growth, it becomes increasingly difficult for MHG to build customer and shareholder value. In this thesis I identify and analyse the spatial externalities, identify the regulatory gaps, and make suggestions for improvements. Further I analyse the effect on MHNF and how it jeopardizes the MHG strategy. A strategic analysis of MHNF reveals that they have the resources and existing strategy to deal with some of the gaps and externalities but not all, and only if they execute well and on time. Further I suggest how the strategy can be improved by focusing on a few key points including: increasing their government lobby for tougher lice rules and more coordinated site fallows, improving their RAS technology by bringing it in-house through purchase of a key supplier, focusing on wrasse culture, and by closing all waiting cages and open well boat transport. Further, I suggest forming joint ventures with smaller farmers to coordinate operations, developing the use of in-sea post smolt systems by bringing it in-house through purchase of a key supplier, and using more robust nets and sterile salmon. Next, I analyse the opportunity to expand to Newfoundland (NL), and determine that NL has the desired institutional surroundings for expansion, and that MHG has the correct resources and management to conduct the expansion, but that it is not without risks. Lastly, I conduct an innovation creation exercise, using a global cross-functional design team from MHG, to develop a hard to copy and profitable innovation for the new business unit, Marine Harvest Atlantic Canada. The product is made from pre-rigor fillet, is antibiotic free, is ASC certified, and from salmon operations monitored by the RSPCA for fish welfare. Finally, I determine that given MHG’s resources and global knowledge, they do not have to choose between growth in Norway or abroad but can do both. In Norway they can take specific actions to separate their fish in space and time from other farms, to avoid spatial conflict and therefore facilitate growth. In addition, MHG can employ cross-functional design teams in all BU’s to create buy-in across functional areas, to help develop successful hard-to-copy innovations and brands, thereby potentially moving the company from a good salmon farming company to a great seafood company, and thus building both customer and shareholder value.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.014 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".